Top Math Resources for Aspiring Data Scientists

You’re Gonna Need Some Math!

As I’ve written about previously, at some point every aspiring data scientist has to learn some math. To be blunt, the more serious you are about data science, the more math you’ll need to learn. If you have a strong math background, this is likely to little issue. In my case, I’ve had to relearn much of the mathematics (note – I’m not done yet!) that I took at university as my professional life had allowed my math skills to atrophy.

Based on my experience teaching our bootcamp there is also a group of aspiring data scientists that fall into a category where their formal math training needs to be augmented. For example, we have many students that come from marketing backgrounds where, for example, studying linear algebra was never a requirement.

Please note that the above is not an exhaustive list. To be honest, you likely can never know enough math to help you as a data scientist. What I would argue is the above list represents the 80/20 rule – the 20% of math that you will use 80% of the time as a practicing data scientist.

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Top Math Resources

Here’s my list of the top 80/20 math resources for aspiring data scientists:

The Cartoon Guide to Statistics is one of the books we provide to our bootcamp students and it is an excellent resource for gently learning – or refreshing – your statistics knowledge. It covers many of the basic concepts in statistics in easy-to-consume and an entertaining fashion. Well worth a read.

Interestingly, I’ve found that University of California Irvine’s free UCI Open course Math 4: Math for Economists is a most excellent resource for focusing on the specific aspects of linear algebra and multivariate calculus needed for aspiring data scientists. The accompanying textbook is also quite good and covers a number of interesting subjects, including single variable calculus for folks that need a refresher.

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